def loadDataSet():
    simpDat = [['r', 'z', 'h', 'j', 'p'],
               ['z', 'y', 'x', 'w', 'v', 'u', 't', 's'],
               ['z'],
               ['r', 'x', 'n', 'o', 's'],
               ['y', 'r', 'x', 'z', 'q', 't', 'p'],
               ['y', 'z', 'x', 'e', 'q', 's', 't', 'm']]
    return simpDat

def createC1(dataSet):
    C1 = []
    for transaction in dataSet:
        for item in transaction:
            if not [item] in C1:
                C1.append([item])
    C1.sort()
    return map(frozenset,C1)

def scanD(D,Ck,minSupport):
    ssCnt = {}
    for tid in D:
        for can in Ck:
            if can.issubset(tid):
                if not ssCnt.has_key(can):ssCnt[can] = 1
                else:ssCnt[can] += 1
    numItems = float(len(D))
    retList = []
    supportData = {}
    for key in ssCnt:
        support = ssCnt[key]/numItems
        if support >= minSupport:
            retList.insert(0,key)
        supportData[key] = support
    return retList,supportData

def aprioriGen(Lk,k):
    retList = []
    lenLk = len(Lk);
    for i in range(lenLk):
        for j in range(i+1,lenLk):
            L1 = list(Lk[i])[:k-2];L2 = list(Lk[j])[:k-2]
            L1.sort();L2.sort()
            if L1==L2:
                retList.append(Lk[i] | Lk[j])
    return retList

def apriori(dataSet,minSupport = 0.002):
    C1 = createC1(dataSet)
    D = map(set,dataSet)
    L1,supportData = scanD(D,C1,minSupport)
    L = [L1]
    k = 2
    while (len(L[k-2]) > 0):
        Ck = aprioriGen(L[k-2],k)
        Lk,supk = scanD(D,Ck,minSupport)
        supportData.update(supk)
        L.append(Lk)
        k += 1
    return L,supportData

def generateRules(L,supportData,minConf = 0.3):
    bigRuleList = []
    for i in range(1,len(L)):
        for freqSet in L[i]:
            H1 = [frozenset([item]) for item in freqSet]
            if (i > 1):
                ruleFromConseq(freqSet,H1,supportData,bigRuleList,minConf)
            else:
                calcConf(freqSet,H1,supportData,bigRuleList,minConf)
    return bigRuleList

def calcConf(freqSet,H,supportData,brl,minConf = 0.7):
    prunedH = []
    for conseq in H:
        conf = supportData[freqSet]/supportData[freqSet-conseq]
        if conf >= minConf:
            print freqSet-conseq,'-->',conseq,'conf:',conf
            brl.append((freqSet-conseq,conseq,conf))
            prunedH.append(conseq)
    return prunedH

def ruleFromConseq(freqSet,H,supportData,brl,minConf = 0.7):
    m = len(H[0])
    if (len(freqSet) > (m + 1)):
        Hmpl = aprioriGen(H,m + 1)
        Hmpl = calcConf(freqSet,Hmpl,supportData,brl,minConf)
        if (len(Hmpl) > 1):
            rulesFromConseq(freqSet,Hmpl,supportData,brl,minConf)
